Tensorflow:terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc

在tensorflow教程深入mnist这一部分，如果照搬CNN代码，会出现terminate called after throwing an instance of 'std::bad_alloc'   what():  std::bad_alloc  Process finished with exit code 134 (interrupted by signal 6: SIGABRT)这个错误，这是因为一次测试10000幅mnist图像会导致电脑内存不足甚至死机，对此我们可以减少测试的数据集。

可以添加如下代码：

            test_batch = mnist.test.next_batch(1000)            acc_forone=compute_accuracy(test_batch[0], test_batch[1])

完整代码如下：

#coding=utf-8import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)def compute_accuracy(v_xs,v_ys):    global prediction    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1}) #这里的keep_prob是保留概率，即我们要保留的RELU的结果所占比例    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})    return resultdef weight_variable(shape):    inital=tf.truncated_normal(shape,stddev=0.1)     #stddev爲標準差    return tf.Variable(inital)def bias_variable(shape):    inital=tf.constant(0.1,shape=shape)    return tf.Variable(inital)def conv2d(x,W):    #x爲像素值，W爲權值    #strides[1,x_movement,y_movement,1]    #must have strides[0]=strides[3]=1    #padding=????    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#def max_pool_2x2(x):    # strides[1,x_movement,y_movement,1]    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')#ksize二三维为池化窗口#define placeholder for inputs to networkxs=tf.placeholder(tf.float32,[None,784])/255ys=tf.placeholder(tf.float32,[None,10])keep_prob=tf.placeholder(tf.float32)x_image=tf.reshape(xs, [-1,28,28,1]) #-1为这个维度不确定,变成一个4维的矩阵，最后为最里面的维数#print x_image.shape                 #最后这个1理解为输入的channel，因为为黑白色所以为1##conv1 layer##W_conv1=weight_variable([5,5,1,32]) #patch 5x5,in size 1 是image的厚度,outsize 32 是提取的特征的维数b_conv1=bias_variable([32])h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)# output size 28x28x32 因为padding='SAME'h_pool1=max_pool_2x2(h_conv1)      #output size 14x14x32##conv2 layer##W_conv2=weight_variable([5,5,32,64]) #patch 5x5,in size 32 是conv1的厚度,outsize 64 是提取的特征的维数b_conv2=bias_variable([64])h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)# output size 14x14x64 因为padding='SAME'h_pool2=max_pool_2x2(h_conv2)      #output size 7x7x64##func1 layer##W_fc1=weight_variable([7*7*64,1024])b_fc1=bias_variable([1024])#[n_samples,7,7,64]->>[n_samples,7*7*64]h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)  #防止过拟合##func2 layer##W_fc2=weight_variable([1024,10])b_fc2=bias_variable([10])#prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)prediction=tf.matmul(h_fc1_drop,W_fc2)+b_fc2#h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)  #防止过拟合#the errro between prediction and real data#cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys, logits=prediction))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)sess=tf.Session()sess.run(tf.global_variables_initializer())for i in range(1000):    batch_xs,batch_ys=mnist.train.next_batch(100)    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})    if i%50 ==0:        accuracy = 0        for j in range(10):            test_batch = mnist.test.next_batch(1000)            acc_forone=compute_accuracy(test_batch[0], test_batch[1])            #print 'once=%f' %(acc_forone)            accuracy=acc_forone+accuracy        print '测试结果:batch:%g,准确率:%f' %(i,accuracy/10)